def __init__(self, master, bg, width, height, scale=30.0, varDict=dict()): Canvas.__init__(self, master, width=width, height=height, bg=bg) self.varDict = varDict # Dictionary of variables self.evaluator = Evaluation.Eval( varDict=varDict ) # Creation of evaluator object, sending variable dictionary as reference self.evaluate = self.evaluator.evaluate # Obtaining the function self.fnList = [] # List of functions to be plotted self.fnColors = {} self.xrange = width #Range of x axis self.yrange = height #Range of y axis # Initializing Origin and Scale self.x_offset = width / 2.0 self.y_offset = height / 2.0 self.scale = scale # Storing a backup self.default_x_offset = self.x_offset self.default_y_offset = self.y_offset self.default_scale = self.scale #Plotting axis self.plotAxis()
'-dt', type=str, help='Exact datetime of model used for inference') args = parser.parse_args() ##### Set specified GPU to be active if args.GPU != -1: os.environ['CUDA_VISIBLE_DEVICES'] = str(args.GPU) ##### Load Training/Testing Data Loader = IO.ShapeNetIO('./Dataset/ShapeNet', batchsize=args.batchsize) Loader.LoadTestFiles() ##### Evaluation Object Eval = Evaluation.Eval() ## Number of categories PartNum = Loader.NUM_PART_CATS output_dim = PartNum ShapeCatNum = Loader.NUM_CATEGORIES #### Save Directories #dt='2020-06-17_07-45-44' dt = args.Datetime BASE_PATH = os.path.expanduser('./Results/ShapeNet/{}_sty-{}_m-{}_{}'.format( args.Network, args.Style, args.m, dt)) SUMMARY_PATH = os.path.join(BASE_PATH, 'Summary') PRED_PATH = os.path.join(BASE_PATH, 'Prediction') CHECKPOINT_PATH = os.path.join(BASE_PATH, 'Checkpoint') summary_filepath = os.path.join(SUMMARY_PATH, 'Summary.txt')
def __init__(self, varDict=dict(pi=3.14159265359, e=2.718281)): self.varDict = varDict #Variable Mapping self.evaluator = Evaluation.Eval(varDict=varDict) #Evaluator object self.evaluate = self.evaluator.evaluate #Evauator Function\